Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have revolutionized automated image recognition tasks, making them invaluable tools for wildlife identification, a crucial aspect of conservation biology and ecological research. CNNs excel at extracting intricate features from images, enabling accurate classification. However, training these deep neural networks can be computationally expensive and time-consuming, while large models can be challenging to deploy on resource-constrained devices. This study aims to review and evaluate various computer vision architecture models that can be applied to wildlife detection and identification, contributing to improved conservation efforts and biodiversity monitoring. The study explores advanced deep learning models such as YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), R-CNN (Region-based Convolutional Neural Networks), ResNet, and Inception. These models are evaluated based on their architecture, performance metrics, and adaptability to wildlife detection tasks. Case studies on bird identification, large mammal detection, and endangered species monitoring are used to illustrate their practical applications. The study’s conclusions indicate that no single model is universally superior; rather, the selection of the appropriate model depends on specific monitoring requirements such as real-time processing, accuracy, and environmental factors. Future research should focus on improving model generalization, scalability, and integrating multi-modal data for more comprehensive wildlife conservation strategies.